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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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相关实验视频

Updated: Jul 11, 2025

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
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生物信息学工具用于测序复杂度估计.

Yuriy L Orlov1,2,3, Nina G Orlova4

  • 1The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow, 119991 Russia.

Biophysical reviews
|November 17, 2023
PubMed
概括
此摘要是机器生成的。

本综述探讨了文本复杂性估计方法和用于分析DNA和蛋白质序列的生物信息学工具. 这些方法对于理解基因组功能,基因调节和进化模式至关重要.

关键词:
没有对齐的自由对齐.生物信息学是一种生物信息学.Entropy Entropy遗传代码中的遗传代码.基因组比较 基因组比较基因组的重新安排.伦佩尔Ziv压缩压缩方式复杂性较低的地区.在线工具 在线工具.序列信息 序列信息 序列信息对文物进行测序.文本的复杂性 文本的复杂性

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 序列复杂性分析对于染色体功能和基因转录调节的生物物理模型至关重要.
  • 了解DNA和蛋白质中低复杂度区域是基因组规模分析和进化研究的关键.

研究的目的:

  • 审查当前的生物信息学工具和方法来估计序列复杂性.
  • 讨论基因组分析,突变热点识别和蛋白质序列分析中的应用.
  • 突出新型机器学习应用和大规模基因组复杂性比较.

主要方法:

  • 使用信息和度来估计文本的复杂性.
  • 采用压缩方法,主要是修改的Lempel-Ziv压缩.
  • 在序列复杂性分析中应用组合和语言测量方法.

主要成果:

  • 复杂性分析有助于基因组序列分割,重复识别和下一代测序阅读的分析.
  • 这些方法适用于突变热点分析,短读质量控制和无对齐基因组比较.
  • 序列复杂性分析扩展到蛋白质,揭示了具有重要的生物和结构功能的保存区域.

结论:

  • 序列复杂性估计工具在网上广泛可用于大规模基因组分析.
  • 机器学习越来越多地整合了序列压缩和复杂性算法用于基因组分类.
  • 复杂性分析提供了对基因调节,蛋白质结构和病毒基因组进化 (包括冠状病毒) 的见解.